Association between blood pressure categories and cardiovascular disease mortality in China

Jie Guo; Jun Lv; Yu Guo; Zheng Bian; Bang Zheng; Man Wu; Ling Yang; Yiping Chen; Jian Su; Jianqiang Zhang; Jvying Yao; Junshi Chen; Zhengming Chen; Canqing Yu; Liming Li; on behalf of the China Kadoorie Biobank Collaborative Group

doi:10.1371/journal.pone.0255373

. 2021 Jul 30;16(7):e0255373. doi: 10.1371/journal.pone.0255373

Association between blood pressure categories and cardiovascular disease mortality in China

Jie Guo ¹, Jun Lv ^1,^2,³, Yu Guo ⁴, Zheng Bian ⁴, Bang Zheng ¹, Man Wu ¹, Ling Yang ⁵, Yiping Chen ⁵, Jian Su ⁶, Jianqiang Zhang ⁷, Jvying Yao ⁸, Junshi Chen ⁹, Zhengming Chen ⁵, Canqing Yu ^1,^2,^*, Liming Li ^1,^2,^*; on behalf of the China Kadoorie Biobank Collaborative Group^¶

Editor: Yan Li¹⁰

¹Department of Epidemiology and Biostatistics, Peking University Health Science Center, Beijing, China

²Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China

³Key Laboratory of Molecular Cardiovascular Sciences, Peking University, Beijing, China

⁴Chinese Academy of Medical Sciences, Beijing, China

⁵Nuffield Department of Population Health, Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), University of Oxford, Oxford, United Kingdom

⁶Jiangsu Center for Disease Control and Prevention, Nanjing, China

⁷Zhouquan Town Health Center, Tongxiang, China

⁸Gaoqiao Town Health Center, Tongxiang, China

⁹China National Center for Food Safety Risk Assessment, Beijing, China

¹⁰Shanghai Institute of Hypertension, CHINA

Competing Interests: The authors have declared that no competing interests exist.

¶ The members of the steering committee and collaborative group are listed in the acknowledgements

^✉

* E-mail: yucanqing@pku.edu.cn (CY); lmleeph@vip.163.com (LL)

Roles

Jie Guo: Conceptualization, Formal analysis, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing

Jun Lv: Conceptualization, Funding acquisition, Investigation, Validation, Writing – original draft, Writing – review & editing

Yu Guo: Data curation, Investigation, Project administration, Writing – review & editing

Zheng Bian: Data curation, Investigation, Project administration, Writing – review & editing

Bang Zheng: Formal analysis, Methodology, Software, Validation, Writing – review & editing

Man Wu: Formal analysis, Methodology, Validation, Writing – review & editing

Ling Yang: Data curation, Formal analysis, Validation, Writing – review & editing

Yiping Chen: Conceptualization, Data curation, Formal analysis, Writing – review & editing

Jian Su: Formal analysis, Investigation, Methodology, Writing – review & editing

Jianqiang Zhang: Data curation, Formal analysis, Investigation, Writing – review & editing

Jvying Yao: Data curation, Formal analysis, Investigation, Writing – review & editing

Junshi Chen: Conceptualization, Formal analysis, Investigation, Validation, Writing – review & editing

Zhengming Chen: Conceptualization, Data curation, Formal analysis, Investigation, Validation, Writing – review & editing

Canqing Yu: Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing

Liming Li: Conceptualization, Data curation, Funding acquisition, Project administration, Supervision, Validation, Writing – review & editing

Yan Li: Editor

PMCID: PMC8323908 PMID: 34329344

Abstract

Background

Blood pressure (BP) categories are useful to simplify preventions in public health, and diagnostic and treatment approaches in clinical practice. Updated evidence about the associations of BP categories with cardiovascular diseases (CVDs) and its subtypes is warranted.

Methods and findings

About 0.5 million adults aged 30 to 79 years were recruited from 10 areas in China during 2004–2008. The present study included 430 977 participants without antihypertension treatment, cancer, or CVD at baseline. BP was measured at least twice in a single visit at baseline and CVD deaths during follow-up were collected via registries and the national health insurance databases. Multivariable Cox regression was used to estimate the associations between BP categories and CVD mortality.

Overall, 16.3% had prehypertension-low, 25.1% had prehypertension-high, 14.1% had isolated systolic hypertension (ISH), 1.9% had isolated diastolic hypertension (IDH), and 9.1% had systolic-diastolic hypertension (SDH). During a median 10-year follow-up, 9660 CVD deaths were documented. Compared with normal, the hazard ratios (95% CI) of prehypertension-low, prehypertension-high, ISH, IDH, SDH for CVD were 1.10 (1.01–1.19), 1.32 (1.23–1.42), 2.04 (1.91–2.19), 2.20 (1.85–2.61), and 3.81 (3.54–4.09), respectively. All hypertension subtypes were related to the increased risk of CVD subtypes, with a stronger association for hemorrhagic stroke than for ischemic heart disease. The associations were stronger in younger than older adults.

Conclusions

Prehypertension-high should be considered in CVD primary prevention given its high prevalence and increased CVD risk. All hypertension subtypes were independently associated with CVD and its subtypes mortality, though the strength of associations varied substantially.

Introduction

Hypertension is the most important risk factor for cardiovascular disease (CVD) [1]. According to single or combined elevations of systolic blood pressure (SBP) and diastolic blood pressure (DBP), hypertension is frequently classified into isolated systolic hypertension (ISH), isolated diastolic hypertension (IDH), and systolic-diastolic hypertension (SDH). The associations with CVD might vary among hypertension subtypes because of their different pathophysiological mechanisms [2,3]. Previous studies had demonstrated definite evidence for the CVD risk of ISH and SDH but the effect of IDH was inconclusive [4–6].

Moreover, a continuous and positive association with CVD had been demonstrated above SBP 115–120 mm Hg [7,8]. There is a growing concern about the CVD risk of SBP 120 mm Hg to 140 mm Hg. The Seventh Report of the Joint National Committee on the Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC-7) introduced a category as prehypertension with BP level of 120 to 139/80 to 89 mm Hg [9]. However, evidence about the CVD risk of prehypertension remains controversial [10,11]. A meta-analysis demonstrated that the increased CVD mortality was largely driven by the high-range of prehypertension [12]. In the 2017 American College of Cardiology/American Heart Association (ACC/AHA) BP guideline, SBP 130–139 and/or DBP 80–89 mm Hg was newly defined as “Hypertension stage 1” [13]. Current evidence found that “Hypertension stage 1” was associated with the increased CVD risk among the younger adults but studies conducted among the older did not find an increased risk, partly due to their small sample size of older adults [14–16]. According to the 2017 ACC/AHA guideline, both the prevalence of hypertension and the number of participants who should take antihypertension treatments increase dramatically [17]. Moreover, during the COVID-19 pandemic, individuals with CVD were more vulnerable to COVID-19 and had a greater risk of developing into a severe condition [18]. Therefore, to clarify the role of “Hypertension stage 1” on the development of CVD, especially among the older adults, is warranted for making prevention strategies about this BP group.

Besides, cerebrovascular diseases have been the top leading cause of mortality in China [19]. Moreover, hemorrhagic stroke accounted for a larger proportion in the Chinese population than western populations [20] and the mortality from hemorrhagic stroke was higher than that from ischemic stroke [21]. However, there is limited evidence for the associations of BP categories with the major subtypes of cardiovascular disease (i.e., ischemic heart disease, ischemic stroke, and hemorrhagic stroke) in a Chinese population. We hypothesized that the newly defined hypertension was associated with increased CVD risk across a wide range of age, and the strength of the association of different hypertension subtypes with CVD and its subtypes might vary. We aimed to provide more detailed information about the associations of BP categories with overall and specific CVD mortality based on the China Kadoorie Biobank (CKB) study.

Materials and methods

Study population

Details of the CKB study design and methods have been reported elsewhere [22,23]. The CKB is a community-based prospective cohort study, involving over 0.5 million adults from 10 areas of China between 2004 and 2008. All men and women aged 30–79 years who were permanently resident and without major disability in each administrative unit were identified and invited to participate [22]. Ethics approvals were obtained from the Ethical Review Committee of Oxford University, the China National Center for Disease Control and Prevention (CDC), and from institutional research boards at the local CDCs in the 10 regions, and all participants provided written informed consent. The study was in accordance with the Declaration of Helsinki.

For the current study, we excluded participants with missing data for covariates (n = 49), or with implausible censoring date (n = 1), or with CVD or cancer at baseline (n = 25 511). Moreover, we further excluded participants with the antihypertension treatment at baseline (n = 65 168), because both dose and types of antihypertensive medications may influence BP levels and lead to misclassification of BP categories. Finally, we included 430 977 participants in the main analyses. By December 31, 2016, 4434 (1.03%) participants were lost to follow-up.

Assessment of BP categories

BP was measured twice by trained staff using a UA-779 digital monitor after they remained at rest in the seated position for at least 5 minutes [24]. If the difference between the two measurements was >10 mm Hg for SBP, a third measurement was required. Only the last two readings were recorded and used to calculate the average of SBP and DBP [7].

According to the JNC-7, BP categories were defined into five groups 1) normal (SBP <120 and DBP <80 mm Hg); 2) prehypertension (SBP 120−139 and/or DBP 80−89 mm Hg); 3) ISH (SBP ≥140 and DBP <90 mm Hg); 4) IDH (SBP <140 and DBP ≥90 mm Hg); 5) SDH (SBP ≥140 and DBP ≥90 mm Hg) [9]. In the 2017 ACC/AHA hypertension guideline, hypertension was defined as SBP ≥130 mmHg and/or DPB ≥90 mmHg [13]. To estimate the effect of “Elevated” and “Hypertension stage 1” in the 2017 ACC/AHA hypertension guideline, we further divided prehypertension into prehypertension-low (equal to “Elevated”, SBP 120−129 and DBP <80 mm Hg) and prehypertension-high (equal to “Hypertension stage 1”, SBP 130−139 and/or DBP 80−89 mm Hg) [13].

Assessment of covariates

Potential confounding variables in this study included sociodemographic characteristics (age, sex, education, and marital status), lifestyle behaviors (tobacco smoking, alcohol consumption, physical activity, and consumption of red meat, fresh fruits, and vegetables), diabetes, menopausal status for female only, and family medical history (heart attack, stroke). Physical activity was calculated by multiplying the metabolic equivalent tasks (METs) value for a particular type of physical activity by hours spent on that activity per day and summing the MET-hours for all activities for each day.

Height, body weight, and heart rate were measured by trained staff. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Prevalent diabetes was defined as a measured fasting blood glucose concentration of ≥7.0 mmol/L, a measured random blood glucose concentration of ≥11.1 mmol/L, or self-reported diagnoses of diabetes.

Assessment of outcomes

The vital status of participants was collected through linkage with regional disease and death registries, and with the new national health insurance databases. To minimize the under-reporting of deaths, we also carried out active follow-up annually, by reviewing residential records, visiting local communities, or directly contacting participants [23].

All deaths were coded using the 10^th International Classification of Diseases (ICD-10). The main outcome measures in our analysis were mortality from CVD (ICD-10 codes I00 to I99), ischemic heart disease (I20 to I25), myocardial infarction (I21 to I23), cerebrovascular disease (I60 to I69), hemorrhagic stroke (I61), and ischemic stroke (I63).

Statistical analysis

Baseline characteristics were described as means and standard errors or percentages in each BP category, with adjustment for age, sex, and survey sites as appropriate, using either multiple linear regression (for continuous variables) or logistic regression (for categorical variables).

Person-years at risk were calculated from the baseline to the date of death, loss to follow-up, or 31 December 2016, whichever occurred first. Cox proportional hazard models, stratified by age at risk (in 5-year intervals), sex, and survey sites (10 regions), were used to estimate the hazard ratios (HR) and 95% confidence intervals (CI) for CVD mortality related to BP categories, with age as the timescale. The proportional hazards assumption was checked using the Schoenfield residuals, and no violation was observed. The associations of BP categories with mortality from total CVD and its subtypes were assessed after adjustment for age (continuous) at recruitment; and subsequently adjusted for the level of education (no formal or primary school, middle school or high school, or college or higher), marital status (married, or others), smoking status (5 categories: never or occasional smoker, ex-smoker who quit not because of illness, ex-smoker who quit because of illness and current smoker divided into 3 groups according to cigarette equivalents/day [<15, 15−25, ≥25]), alcohol consumption (7 categories: never or occasional or seasonal drinker, ex-regular drinker of reduced intake, 1 to 5 days/week, almost daily drinker divided into 4 groups according to total grams of alcohol [<15, 15−<30, 30−<60, ≥60]), intake frequencies of red meat, fresh fruits, and vegetables (daily, 4−6 days/week, 1−3 days/week, or monthly, rarely or never), physical activity in MET-hours a day (continuous), survey seasons (spring; summer: June, July, August; autumn; winter: December, January, February), menopausal status (for female only, postmenopausal or others); and finally further adjustment for prevalent diabetes at baseline, family medical history, BMI (continuous), and heart rate (continuous). The Nelson-Aalen method was used to describe the cumulative hazard of CVDs during the follow-up across BP categories.

Moreover, we conducted subgroup analyses by age (30−49, 50−59, or 60−79 years), sex (male or female), and survey sites (10 areas) and the interaction effect was estimated by adding a cross-product term (e.g., age groups × BP categories) to the Cox model. In sensitivity analyses, we 1) investigated the associations between BP categories and CVD mortality after excluding the first 2 years of follow-up; or excluding those who had diabetes at baseline; 2) compared the risk of ISH, IDH, and SDH with further controlling SBP or DBP by conducting analyses in the range of SBP/DBP level of 140−159/90−99 mm Hg and of ≥160/100 mm Hg separately.

Statistical analyses were performed using SAS 9.3 (SAS Institute, Cary, NC). All P values were two-sided, and we defined statistical significance as P <0.05.

Results

Baseline characteristics of participants by BP categories

Among 430 977 participants, the mean age was 50.6 years, 58.7% were female, and 57.6% were from rural areas. Overall, 16.3% had prehypertension-low, 25.1% had prehypertension-high, 14.1% had ISH, 1.9% had IDH, and 9.1% had SDH. The prevalence of ISH in older participants was higher than that in younger, while the opposite was observed for IDH (Fig 1).

Compared with normal BP, participants with prehypertension-low, prehypertension-high, or hypertension subtypes were older (except for IDH P = 0.10) and were less likely to be female, had a lower level of education (except for IDH P = 0.99), were more likely to live in rural areas (except for IDH P = 0.32), were less likely to smoke but more likely to drink regularly, were more likely to consume red meat (except for prehypertension-low P = 0.33) but less likely to consume fresh fruit, had a higher level of BMI and heart rate, and had a higher prevalence of diabetes (Table 1). The distribution of baseline characteristics of the study population without adjustment for age, sex, and survey sites was presented in S1 Table.

Table 1. Baseline characteristics of the study population by baseline BP categories^a.

Characteristic	Normal	Prehypertension-low	Prehypertension-high	Hypertension
Characteristic	Normal	Prehypertension-low	Prehypertension-high	ISH	IDH	SDH
Total, No.	144 765	70 130	107 960	60 708	8387	39 027
Age, mean (SE), y	47.7 (0.03)	50.5 (0.04)	50.5 (0.03)	58.0 (0.04)	47.9 (0.11)	53.0 (0.05)
Female, No. (%)	96 918 (66.1)	39 937 (56.9)	58 329 (54.0)	35 134 (60.2)	3794 (44.7)	18 784 (49.0)
Education level, No. (%)
No formal education or primary	59 294 (48.5)	35 191 (49.8)	53 136 (49.2)	40 644 (51.4)	3246 (48.6)	21 588 (50.6)
Middle or high school	73 853 (45.0)	31 459 (44.7)	49 166 (45.4)	18 249 (43.6)	4442 (45.6)	15 814 (44.9)
College or higher	11 618 (6.5)	3480 (5.5)	5658 (5.5)	1815 (5.0)	699 (5.8)	1625 (4.5)
Rural area, No. (%)	73 840 (50.1)	42 291 (60.2)	65 486 (60.6)	37 856 (64.4)	4324 (50.6)	24 396 (63.1)
Married, No. (%)	134 038 (91.3)	64 446 (91.8)	99 571 (91.7)	52 636 (91.1)	7849 (91.1)	35 419 (91.0)
Regular smoking, No. (%)	33 951 (29.0)	20 331 (27.7)	32 518 (27.0)	16 082 (26.3)	2917 (26.8)	13 142 (27.2)
Male	31 785 (66.5)	19 323 (63.6)	31 197 (62.4)	15147 (61.3)	2852 (61.8)	12689 (63.1)
Female	2166 (3.1)	1008 (2.4)	1321 (2.2)	935 (1.9)	65 (2.3)	453 (2.2)
Regular alcohol intake, No. (%)	15 732 (13.5)	10 150 (14.1)	19 655 (16.0)	9628 (16.6)	2196 (18.7)	9724 (20.1)
Male	13 635 (29.5)	9330 (31.4)	18 320 (35.6)	8875 (37.2)	2085 (40.8)	9239 (44.4)
Female	2097 (2.3)	820 (2.0)	1335 (2.2)	753 (2.1)	111 (3.1)	485 (2.7)
Average weekly consumption, mean (SE), day/week ^b
Fresh vegetables	6.82 (0.002)	6.82 (0.002)	6.82 (0.002)	6.82 (0.003)	6.81 (0.008)	6.81 (0.004)
Fresh fruits	2.59 (0.006)	2.48 (0.008)	2.48 (0.007)	2.37 (0.009)	2.44 (0.024)	2.30 (0.011)
Red meat	3.90 (0.006)	3.91 (0.008)	3.96 (0.006)	3.96 (0.009)	3.99 (0.023)	3.94 (0.010)
Postmenopausal, No. (%) ^c	33 002 (49.7)	18 865 (48.5)	27 871 (49.1)	26 430 (48.0)	1447 (50.5)	11 023 (49.1)
Physical activity, mean (SE), MET- hr/day	21.9 (0.03)	22.3 (0.05)	21.9 (0.04)	21.8 (0.05)	21.1 (0.13)	21.6 (0.06)
Heart rate, mean (SE), bpm	76.2 (0.03)	77.7 (0.04)	79.7 (0.03)	79.8 (0.05)	82.9 (0.12)	83.1 (0.06)
Body mass index, mean (SE), kg/m²	22.4 (0.01)	23.3 (0.01)	23.8 (0.01)	24.3 (0.01)	24.4(0.03)	24.9(0.02)
Diabetes, No. (%)	3574 (2.6)	2635 (3.9)	4847 (4.7)	5177 (6.9)	366 (4.7)	2409 (6.0)
Family medical history, No. (%)	25 525 (17.2)	12 555 (18.2)	21 193 (19.7)	11 662 (20.2)	2012 (22.3)	9347 (23.9)
Heart attack	4707 (3.0)	2078 (3.1)	3314 (3.1)	1613 (3.1)	338 (3.5)	1329 (3.5)
Stroke	21 875 (14.9)	10 918 (15.8)	18 684 (17.3)	10 445 (17.8)	1772 (19.8)	8390 (21.3)
SBP, mean (SE), mmHg	109.7 (0.02)	124.3 (0.03)	129.8 (0.03)	150.0 (0.04)	133.5 (0.09)	161.8 (0.04)
DBP, mean (SE), mmHg	68.0 (0.02)	72.4 (0.02)	80.1 (0.02)	81.4 (0.03)	92.1 (0.07)	97.6 (0.03)

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Abbreviations: BP, blood pressure; SE, standard error; MET, metabolic equivalent of task; bpm, beat per minute.

^a All variables were adjusted for age, sex, and survey sites when appropriate.

^b Average weekly consumptions of red meat, fresh vegetables, and fruits were calculated by assigning participants to the midpoint of their consumption category.

^c Only for female.

Association of BP categories with CVD mortality

During 4.3 million person-years of follow-up (mean duration of follow-up: 10.0 years; median 10.2 years), there were 9660 deaths from CVD, 3564 from ischemic heart diseases (including 2248 myocardial infarction), and 5168 from cerebrovascular diseases (including 3092 hemorrhagic strokes and 965 ischemic strokes).

With normal BP as the reference, prehypertension had a higher risk of overall CVD, cerebrovascular disease, and hemorrhagic stroke (S2 Table). Similar patterns were also observed for prehypertension-low and prehypertension-high (Table 2). Besides, prehypertension-high was related to the increased risk of ischemic heart disease and ischemic stroke. All hypertension subtypes were associated with the increased mortality of overall CVD and its subtypes, after basic- or multi-adjustment for potential confounding factors (S3 Table). The multi-adjusted HRs for overall CVD were highest for SDH (adjusted HR, 3.81 [95%CI, 3.54 to 4.09]), followed by IDH (2.20 [95%CI, 1.85 to 2.61]), ISH (2.04 [95%CI, 1.91 to 2.19]), prehypertension-high (1.32 [95%CI, 1.23 to 1.42]), and finally prehypertension-low (1.10 [95%CI, 1.01 to 1.19]) (Table 2). The Nelson-Aalen curves of the cumulative hazard of CVDs visually showed that SDH had the highest hazard curve (S1 Fig).

Table 2. Associations of BP categories with mortality from CVDs and its major subtypes among 430 977 participants.

Cause of death	Normal	Prehypertension-low	Prehypertension-high	Hypertension
Cause of death	Normal	Prehypertension-low	Prehypertension-high	ISH	IDH	SDH
No. of participants	144 765	70 130	107 960	60 708	8387	39 027
No. of person-years	1 462 055	705 310	1 082 972	593 913	85 056	383 785
Cardiovascular disease
No. of deaths	1330	1039	1787	3133	148	2223
Incidence rate (no./1,000 person-y)	0.91	1.47	1.65	5.28	1.74	5.79
HR (95%CI)	Reference	1.10 (1.01–1.19)	1.32 (1.23–1.42)	2.04 (1.91–2.19)	2.20 (1.85–2.61)	3.81 (3.54–4.09)
Ischemic heart disease
No. of deaths	586	423	691	1159	56	649
Incidence rate (no./1,000 person-y)	0.40	0.60	0.64	1.95	0.66	1.69
HR (95%CI)	Reference	1.00 (0.88–1.13)	1.14 (1.02–1.28)	1.66 (1.50–1.85)	1.76 (1.32–2.30)	2.48 (2.21–2.80)
Myocardial infarction
No. of deaths	378	258	444	713	32	423
Incidence rate (no./1,000 person-y)	0.26	0.37	0.41	1.20	0.38	1.10
HR (95%CI)	Reference	0.94 (0.80–1.11)	1.14 (0.99–1.31)	1.65 (1.44–1.88)	1.58 (1.08–2.24)	2.45 (2.11–2.84)
Cerebrovascular disease
No. of deaths	577	499	900	1683	70	1439
Incidence rate (no./1,000 person-y)	0.39	0.71	0.83	2.83	0.82	3.75
HR (95%CI)	Reference	1.20 (1.06–1.36)	1.53 (1.37–1.70)	2.52 (2.28–2.78)	2.51 (1.94–3.21)	5.60 (5.06–6.21)
Hemorrhagic stroke
No. of deaths	315	279	530	962	45	961
Incidence rate (no./1,000 person-y)	0.22	0.40	0.49	1.62	0.53	2.50
HR (95%CI)	Reference	1.26 (1.07–1.48)	1.69 (1.47–1.95)	2.90 (2.53–3.32)	2.86 (2.06–3.88)	6.91 (6.05–7.92)
Ischemic stroke
No. of deaths	126	93	181	331	13	221
Incidence rate (no./1,000 person-y)	0.09	0.13	0.17	0.56	0.15	0.58
HR (95%CI)	Reference	1.01 (0.77–1.33)	1.38 (1.10–1.75)	2.04 (1.65–2.55)	2.13 (1.14–3.65)	3.93 (3.12–4.97)

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Abbreviations: BP, blood pressure; CVDs, cardiovascular diseases; ISH, isolated systolic hypertension; IDH, isolated diastolic hypertension; SDH, systolic-diastolic hypertension; HR, hazard ratio; CI, confidence interval.

Multi-adjusted hazard ratios were adjusted for age (continuous variable); education level (no formal or primary school, middle school or high school, or college or higher); marital status (married, others); smoking status (never or occasional smoker, ex-smoker who quit not because of illness, ex-smoker who quit because of illness and current smoker divided into 3 groups according to cigarette equivalents/day [<15, 15–25, ≥25]); alcohol consumption (never or occasional or seasonal drinker, ex-regular drinker of reduced intake, 1 to 5 days/week, almost daily drinker divided into 4 groups according to total grams of alcohol [<15, 15-<30, 30-<60, ≥60]); intake frequencies of vegetables, fruits, and red meat (daily, 4 to 6 days/per week, 1 to 3 days/per week, or monthly or rarely/never); physical activity (continuous variable); body mass index (BMI, continuous variable); survey season (spring; summer: June, July, August; autumn; winter: December, January, February); heart rate (continuous variable); diabetes at baseline (2 categories, presence or absence); family history of heart attack, or stroke (2 categories, presence or absence, only adjusted for in corresponding analysis of cause-specific mortality), and were stratified according to age at risk (in 5-year intervals), sex, and survey sites.

Subgroup and sensitivity analyses

Prehypertension-high and all hypertension subtypes were associated with increased CVD mortality among all age groups (Fig 2). Moreover, we observed stronger associations in younger participants than older ones (all P values for interaction <0.005, except for ischemic stroke P = 0.20). Heterogeneity by sex was observed for overall CVD, ischemic heart disease, cerebrovascular diseases, and hemorrhagic stroke (S4 Table). S5 Table shows the associations between BP categories and CVD mortality across 10 survey sites. There was a statistically significant interaction between BP categories and survey sites (P for interaction = 0.016).

The associations between BP categories and CVD mortality were not materially altered after excluding the first 2 years of follow-up (S6 Table) or excluding participants who had diabetes at baseline (S7 Table). In hypertension, the multivariable-adjusted HR of SDH for CVD mortality was higher than that of ISH and IDH (P <0.001), while the effect of ISH on CVD mortality was almost similar to that of IDH (S2 Fig).

Discussion

The present study, involving more than 0.4M people living in China, found that both prehypertension-low and prehypertension-high were associated with higher CVD mortality independent of other cardiovascular risk factors. All hypertension subtypes were associated with increased mortality from overall CVD and CVD subtypes, and the CVD risk of SDH was higher than that of ISH and IDH. The associations between BP categories and cerebrovascular diseases were stronger than for ischemic heart diseases. Furthermore, the associations between BP categories and CVD mortality were stronger in younger participants than in older ones.

The 2017 ACC/AHA hypertension guideline defined the 130-139/80-89 mm Hg as “Hypertension stage 1” [13]. Based on the guideline, about a quarter of participants (i.e., prehypertension-high) in our study would be newly defined as hypertension. Previous studies found an increased CVD risk of this BP category in younger adults, which was consistent with our results [15,25]. However, the findings were mixed in older adults. Some studies reported that this BP category was not associated with the increased CVD risk [15,25], while others reported a slightly higher CVD mortality for those aged ≥65 years (HR [95%CI]: 1.22 [1.04 to 1.44]) [26]. In our study, the CVD risk of this BP category became weaker in participants aged 60 to 79 years, but there was still a 22% higher CVD mortality than the normal BP. Moreover, this BP category was associated with increased mortality from CVD subtypes, especially for hemorrhagic stroke, with a 69% higher risk compared to normal BP. Given the high prevalence of cerebrovascular diseases, especially hemorrhagic stroke in China [20], managing the BP of that newly defined hypertension in the public health practice may benefit for reducing the CVD burden.

Previous studies have reported consistent associations of both ISH and SDH with the CVD mortality [4,6,27], and clinical trial also detected that individuals with ISH or SDH could benefit from antihypertensive treatment [28], but whether IDH was associated with an increased CVD risk was still controversial [4,5,29]. In the current study, all hypertension subtypes were related to the increased mortality from CVD and specific CVDs, and IDH was at least as important as ISH in predicting future CVD mortality. These results also supported current guidelines which recommended pharmacologic treatments based on DBP as well as SBP [9,30]. In line with previous studies [4,6], the present study demonstrated that SDH conferred the highest CVD risk, followed by IDH and ISH. The heterogeneity among hypertension subtypes might be explained by the higher mean level of SBP or DBP of SDH than that of ISH or IDH. However, after controlling for SBP or DBP, the CVD risk of SDH was still higher than that of ISH. Similar results were also observed for cerebrovascular disease, hemorrhagic stroke, and ischemic stroke. Our finding suggested that incorporating SBP and DBP might improve the prediction of CVD risk models, and future studies are needed to clarify potential mechanisms for the heterogeneity among hypertension subtypes. Likewise, we also identified that the strength of the associations between hypertension subtypes and specific CVD mortality varied significantly, with stronger for cerebrovascular disease than for ischemic heart disease, and extreme for hemorrhagic stroke than for ischemic stroke.

Previous literature found that the associations between BP categories and CVD deaths varied by age, with a stronger association in younger adults than older ones [6,31]. Consistent with these findings, we also identified stronger associations between BP categories and major CVDs (except ischemic stroke) among younger compared with older. Additionally, the mechanisms of ISH in young are still unclear. Some studies showed that in young and middle-aged adults, ISH was “pseudo” or “spurious” hypertension attributed to amplification of central aortic waveform, while others found ISH might be related to increased stroke volume and aortic stiffness [32–34]. However, the increased CVD risk of ISH in the current study indicated the terms “pseudo” or “spurious” hypertension might be unjustified.

To the best of our knowledge, this is the largest prospective study to investigate the associations of BP categories with mortality from CVD and its major subtypes in a Chinese population. The chief strengths of this study included the unified standard methods for collecting information, the sufficient number of CVD outcomes, and the rigorous check of diagnosis of CVD. This study also had several limitations. First, BP was obtained in a single baseline survey, without considering the fluctuation of BP in the daytime, which may misclassify individuals with “white-coat hypertension” or “masked hypertension” [35,36]. However, it is not feasible to monitor ambulatory BP for each participant in a large-scale population study. Second, we excluded participants who taking antihypertensive medicines, which might cause selection bias and limit the extrapolation of our findings. However, antihypertensive medication would affect the patients’ blood pressure level, leading to misclassification of BP categories. Hence, it is more reasonable to restrict study participants without antihypertensive treatment. Third, elevated low-density lipoprotein (or total) cholesterol, a risk factor for CVD [37], was not available in the present study. A meta-analysis reported that total cholesterol (TC) was positively associated with ischemic heart disease mortality, but there was rather a weak association of TC with mortality from cerebrovascular disease and hemorrhagic stroke [38]. Therefore, failing to adjust for TC is unlikely to have any appreciable impact on the associations with cerebrovascular disease in our study. Fourth, under-reporting of cardiovascular deaths might have occurred during follow-up, but the probability of under-reported would not depend on BP categories, and we also used multiple ways to minimize the under-reporting of deaths.

Conclusion

The definition of hypertension is one of the most notable changes in the 2017 ACC/AHA hypertension guideline. The present study provided important evidence about the long-term CVD risk of those new hypertensives (i.e., “Hypertension stage 1” in 2017 guideline and prehypertension-high in the current study) and highlighted its important role in CVD primary prevention, both due to the high prevalence and be associated with higher CVD mortality. All hypertension subtypes were related to the increased mortality from CVDs, especially from hemorrhagic strokes, and should be considered in BP management regardless of age and gender.

Supporting information

S1 Checklist. STROBE statement for observational studies.

(DOCX)

Click here for additional data file.^{(20.9KB, docx)}

S1 Fig. Nelson-Aalen cumulative hazard for cardiovascular diseases according to the blood pressure categories.

(DOCX)

Click here for additional data file.^{(94.3KB, docx)}

S2 Fig. Associations of ISH, IDH, and SDH with mortality from CVDs and its major subtypes in stage 1 hypertension, stage 2 hypertension and total hypertension.

(DOCX)

Click here for additional data file.^{(333.2KB, docx)}

S1 Table. Baseline characteristics of the study population by baseline BP categories.

(DOCX)

Click here for additional data file.^{(19.9KB, docx)}

S2 Table. Associations of blood pressure categories with cardiovascular diseases mortality among 430 977 participants.

(DOCX)

Click here for additional data file.^{(17.4KB, docx)}

S3 Table. Associations of blood pressure categories with mortality from cardiovascular diseases and its major subtypes.

Values are hazard ratios (95% confidence interval).

(DOCX)

Click here for additional data file.^{(17.9KB, docx)}

S4 Table. Associations of prehypertension and hypertension subtypes with mortality from cardiovascular diseases and its major subtypes by sex.

(DOCX)

Click here for additional data file.^{(20.7KB, docx)}

S5 Table. Associations of prehypertension and hypertension subtypes with mortality from cardiovascular diseases by survey sites.

(DOCX)

Click here for additional data file.^{(16.5KB, docx)}

S6 Table. Associations of blood pressure categories with deaths due to cardiovascular diseases among participants excluding the first two years of follow-up.

(DOCX)

Click here for additional data file.^{(16.9KB, docx)}

S7 Table. Associations of blood pressure categories with deaths of cardiovascular diseases among non-diabetes participants at baseline.

(DOCX)

Click here for additional data file.^{(16.9KB, docx)}

S1 Text. Baseline questionnaire in the China Kadoorie Biobank study.

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Click here for additional data file.^{(13KB, docx)}

Acknowledgments

The chief acknowledgment is to the participants, the project staff, and the China National Centre for Disease Control and Prevention (CDC) and its regional offices for assisting with the fieldwork. We thank Judith Mackay in Hong Kong; Yu Wang, Gonghuan Yang, Zhengfu Qiang, Lin Feng, Maigeng Zhou, Wenhua Zhao, and Yan Zhang in China CDC; Lingzhi Kong, Xiucheng Yu, and Kun Li in the Chinese Ministry of Health; and Sarah Clark, Martin Radley, Mike Hill, Hongchao Pan, and Jill Boreham in the CTSU, Oxford, for assisting with the design, planning, organization, and conduct of the study. Members of the China Kadoorie Biobank collaborative group as follows. International Steering Committee: Junshi Chen, Zhengming Chen (PI, E-mail: zhengming.chen@ctsu.ox.ac.uk), Robert Clarke, Rory Collins, Yu Guo, Liming Li (PI, E-mail: lmleeph@vip.163.com), Jun Lv, Richard Peto, Robin Walters. International Co-ordinating Centre, Oxford: Daniel Avery, Ruth Boxall, Derrick Bennett, Yumei Chang, Yiping Chen, Zhengming Chen, Robert Clarke, Huaidong Du, Simon Gilbert, Alex Hacker, Mike Hill, Michael Holmes, Andri Iona, Christiana Kartsonaki, Rene Kerosi, Ling Kong, Om Kurmi, Garry Lancaster, Sarah Lewington, Kuang Lin, John McDonnell, Iona Millwood, Qunhua Nie, Jayakrishnan Radhakrishnan, Paul Ryder, Sam Sansome, Dan Schmidt, Paul Sherliker, Rajani Sohoni, Becky Stevens, Iain Turnbull, Robin Walters, Jenny Wang, Lin Wang, Neil Wright, Ling Yang, Xiaoming Yang. National Co-ordinating Centre, Beijing: Zheng Bian, Yu Guo, Xiao Han, Can Hou, Jun Lv, Pei Pei, Chao Liu, Canqing Yu. 10 Regional Co-ordinating Centres: Qingdao CDC: Zengchang Pang, Ruqin Gao, Shanpeng Li, Shaojie Wang, Yongmei Liu, Ranran Du, Yajing Zang, Liang Cheng, Xiaocao Tian, Hua Zhang, Yaoming Zhai, Feng Ning, Xiaohui Sun, Feifei Li. Licang CDC: Silu Lv, Junzheng Wang, Wei Hou. Heilongjiang Provincial CDC: Mingyuan Zeng, Ge Jiang, Xue Zhou. Nangang CDC: Liqiu Yang, Hui He, Bo Yu, Yanjie Li, Qinai Xu,Quan Kang, Ziyan Guo. Hainan Provincial CDC: Dan Wang, Ximin Hu, Jinyan Chen, Yan Fu, Zhenwang Fu, Xiaohuan Wang. Meilan CDC: Min Weng, Zhendong Guo, Shukuan Wu,Yilei Li, Huimei Li, Zhifang Fu. Jiangsu Provincial CDC: Ming Wu, Yonglin Zhou, Jinyi Zhou, Ran Tao, Jie Yang, Jian Su. Suzhou CDC: Fang liu, Jun Zhang, Yihe Hu, Yan Lu, Liangcai Ma, Aiyu Tang, Shuo Zhang, Jianrong Jin, Jingchao Liu. Guangxi Provincial CDC: Zhenzhu Tang, Naying Chen, Ying Huang. Liuzhou CDC: Mingqiang Li, Jinhuai Meng, Rong Pan, Qilian Jiang, Jian Lan,Yun Liu, Liuping Wei, Liyuan Zhou, Ningyu Chen Ping Wang, Fanwen Meng, Yulu Qin, Sisi Wang. Sichuan Provincial CDC: Xianping Wu, Ningmei Zhang, Xiaofang Chen,Weiwei Zhou. Pengzhou CDC: Guojin Luo, Jianguo Li, Xiaofang Chen, Xunfu Zhong, Jiaqiu Liu, Qiang Sun. Gansu Provincial CDC: Pengfei Ge, Xiaolan Ren, Caixia Dong. Maiji CDC: Hui Zhang, Enke Mao, Xiaoping Wang, Tao Wang, Xi zhang. Henan Provincial CDC: Ding Zhang, Gang Zhou, Shixian Feng, Liang Chang, Lei Fan. Huixian CDC: Yulian Gao, Tianyou He, Huarong Sun, Pan He, Chen Hu, Xukui Zhang, Huifang Wu, Pan He. Zhejiang Provincial CDC: Min Yu, Ruying Hu, Hao Wang. Tongxiang CDC: Yijian Qian, Chunmei Wang, Kaixu Xie, Lingli Chen, Yidan Zhang, Dongxia Pan, Qijun Gu. Hunan Provincial CDC: Yuelong Huang, Biyun Chen, Li Yin, Huilin Liu, Zhongxi Fu, Qiaohua Xu. Liuyang CDC: Xin Xu, Hao Zhang, Huajun Long, Xianzhi Li, Libo Zhang, Zhe Qiu.

Data Availability

Data cannot be shared publicly because there exist ethical restrictions. According to the Regulation of the People's Republic of China on the Administration of Human Genetic Resources, we are not allowed to provide Chinese human clinical and genetic data abroad without an official approval. Researchers that are interested in accessing and analyzing data collected in the China Kadoorie Biobank (CKB) study may contact the data use and access committee (https://www.ckbiobank.org/site/Data+Access/Data+Access+Policy). As stated in the policy, as data custodian, the CKB study group must maintain the integrity of the database for future use and regulate data access to comply with prior conditions agreed with the Chinese government.

Funding Statement

This study was supported by grants from the National Key R&D Program of China (http://service.most.gov.cn/) to YG (2016YFC0900500, 2016YFC0900501) and CY (2016YFC0900504), and from the Chinese Ministry of Science and Technology (http://service.most.gov.cn/) to LL (2011BAI09B01), and from the National Natural Science Foundation of China (http://www.nsfc.gov.cn/) to CY (81973125) and LL (91846303, 91843302). The CKB baseline survey and the first re-survey were supported by a grant from the Kadoorie Charitable Foundation in Hong Kong. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0255373.r001

Decision Letter 0

Yan Li

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12 Apr 2021

PONE-D-21-07340

Association between Blood Pressure Categories and Cardiovascular Disease Mortality in China

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Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors' work with large prospective cohort (n=431,000, 10-year follow-up) provided information on blood pressure (BP) category and prospective mortality risk from cardiovascular (CV) origin. Importantly, prehypertension, along with all subcategories of hypertension, was associated with increased mortality compared to normotensive participants in adult Chinese free of baseline antihypertensive drug, CV disease or cancer.

Major comments

1. High BP could aggravate other non-CV diseases. The hazard ratio (HR) of BP categories to all-cause and non-CV mortality should therefore be reported. CV death, as a competing event to death from other causes, should be evaluated by competing risk regression.

2. There could be discrepancies on the mortality rate and HR of BP categories with mortality among the included centers. Center-specific statistics should as well be provided.

Minor comments

1. The limitation part in the abstract (line 56-58), albeit modestly written, could lead to a suspicion in the first impression on the credibility of results and conclusion. In the abstract, it's better to put "BP was measured twice in a single visit" into the methods section and the limitation should be omitted.

2. Following the line, in methods part, line 119-120, the authors should rephrase the measurement of blood pressure as "occasion" and "reading" are two distinct entities. The authors may refer to their Lacey 2018 Lancet Global Health paper (PMID 29773120) for more precise description on the method of BP measurement.

3. Standard Error SE used in Table 1 is inversely correlated to sample size, and therefore definitely small when the sample size is huge. Change it to SD to show the distribution of continuous variables.

4. line 88: "the number of hypertension" should be the prevalence of hypertension or the number of participants with hypertension.

5. line 96: there is limited evidence for the associations of 'BP categories' with the....

6. line 113: Check if the study was performed in accordance with the Declaration of Helsinki, and add to the end of this paragraph.

Reviewer #2: “Association between Blood Pressure Categories and Cardiovascular Disease Mortality in China” divided the population into 6 categories based on the systolic and diastolic blood pressure levels among approximately 430,000 general people without antihypertensive drugs or medical history of cardiovascular disease. The study found that compared with people with normal blood pressure, the other 5 categories were all related to compound cardiovascular death after adjustment. At the same time, the study found that cerebrovascular mortality was most closely related to blood pressure categories in the Chinese population. Besides, age was a confounding factor that affected the strength of the association between blood pressure categories and cardiovascular death.

The highlight of this study, as the author mentioned, the sample size was very large, and the study covered 10 regions in China.

There however remain some points that might be clarified:

1) In the methodology (lines 108-109), please add that this study was a general population study when you briefly described the CKB study. Otherwise, it will be liable to cause misunderstandings.

2) The average blood pressure used in this study is the average of the last two blood pressure measurements. Will there be different results and conclusions if the average of three consecutive blood pressure measurements is taken?

3) This study only analyzed the relationship between cardiovascular death and blood pressure categories. Why is there no further analysis of all-cause death? If feasible, please add results and discussion about the relationship between all-cause death and blood pressure categories.

4) The statistical description of the constituent ratio in Figure 1 is not visualized enough using histograms, and it is recommended to modify it to a pie chart.

5) When the continuous variables in Table 1 were used for statistical description, should the standard deviation rather than the standard error be considered for the degree of dispersion?

6) The incidence rate of hemorrhagic stroke in Table 2 is higher than that of ischemic stroke. Is it different from the constituent ratio of stroke types in the Chinese population? Please explain further.

7) The study concluded that prehypertension-low was associated with higher cardiovascular deaths. However, in Table S2, prehypertension-low is actually only associated with hemorrhagic stroke, HR1.26 (1.07-1.48), and other single-factor deaths were not related to prehypertension-low. Is there more sufficient evidence to make such an extrapolated conclusion cautiously?

8) In Table S2, the HR of most blood pressure types in Model 1, Model 2 and Model 3 were gradually increasing. In the Cox risk regression model, as the number of independent variables added, the contribution of the research factors of interest to the outcome generally decreases. This phenomenon was confusing, please explain further.

Reviewer #3: This study, involving 0.4 million people, investigated the association between hypertension subtypes and CVD outcomes. It was well written.

There are several comments that might be taken into consideration.

1. It would be better to demonstrated the “inclusion criteria” and “exclusion criteria” of the CKB study.

2. For better understanding, it is recommended to use the definition of hypertension grades according to the 2018 ESC guidelines of hypertension as “optimal, normal, high normal”, rather than using the terms as “normal, prehypertension-low, prehypertension-high”.

3. Please state why patients with antihypertension treatment at baseline were excluded.

4. Please state the proportion of “loss to follow-up” and how it was addressed.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Kun Xie

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PLoS One. 2021 Jul 30;16(7):e0255373. doi: 10.1371/journal.pone.0255373.r002

Author response to Decision Letter 0

10 May 2021

Response to Reviewers’ comments

Ms. Ref. No.: PONE-D-21-07340

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

We thank the editor’s help and we have revised the manuscript based on the PLOS ONE’S requirements carefully.

In your revised cover letter, please address the following prompts:

We will update your Data Availability statement on your behalf to reflect the information you provide.

We thank the editor’s help, and we would like to revise the Data Availability statement as follows.

“The authors do not own the data for this study. Requests for the data should be submitted to the China Kadoorie Biobank (CKB) Data Access Committee (http://www.ckbiobank.org/site/Research/Data+Access+Policy). As stated in the policy, as data custodian, the CKB study group must maintain the integrity of the database for future use and regulate data access to comply with prior conditions agreed with the Chinese government. Data security is an integral part of CKB study protocols. Data can be released outside the CKB research group only with appropriate security safeguards.”

3. One of the noted authors is a group or consortium; China Kadoorie Biobank Collaborative Group. In addition to naming the author group, please list the individual authors and affiliations within this group in the acknowledgments section of your manuscript. Please also indicate clearly a lead author for this group along with a contact email address.

Following the editor’s suggestion, we added the members of the China Kadoorie Biobank collaborative group in the acknowledgements (pages 24-25, lines 392-429).

Please revise the manuscript according to the comments of the three Reviewers. In addition, please consider analyses using blood pressure as a continuous variable.

We thank the reviewers for their valuable comments and thus for the improvement of our paper. All comments and suggestions have been carefully addressed in the revised manuscript. Details of the comments are responded to point-by-point as follows. One previous paper based on CKB study had explored the association between blood pressure as a continuous variable and CVD (Lacey Ben, et al, The Lancet Global Health, 2018), so in this manuscript, we mainly focused on the risk effect of blood pressure categories and aimed to provide more evidence about the newly defined hypertension in 2017 ACC/AHA and hypertension subtypes.

Major comments

Thank the review’s comments. Considering that the burden of high BP on the health system is mainly through CVDs and the heavy burden of CVD, especially cerebrovascular diseases, in the Chinese population, we mainly focused on exploring the effect of BP categories on CVD and CVD subtypes in the current study. We agree that the associations of BP categories with all-cause and non-CVD mortality are important considerations but not within the scope of this study. In the future study, we would explore the role of BP in the development of all-cause and non-CVD mortality.

2. There could be discrepancies on the mortality rate and HR of BP categories with mortality among the included centers. Center-specific statistics should as well be provided.

We thank the reviewer’s comments. We agreed with the reviewer that there might be discrepancies in the associations between BP categories and CVD mortality by survey sites. To deal with this issue, we conducted the Cox regression models with stratification according to survey sites (10 regions), as well as age at risk (in 5-year intervals) and sex.

Moreover, we conducted subgroup analyses by 10 regions and added the results in the supplementary file (S5 Table) and in the manuscript (page 18, lines 277-279) as follows.

“S5 Table shows the associations between BP categories and CVD mortality across 10 survey sites. There was a statistically significant interaction between BP categories and survey sites (P for interaction = 0.016).”

Minor comments

We thank the reviewer for pointing this out. Following the reviewer’s suggestion, we revised the methods (page 3, lines 46-47) as follows “BP was measured twice in a single visit at baseline”. As the reviewer commented, to avoid confusion, we delete the limitation in the abstract.

We appreciate the reviewer’s careful reading, and we have corrected it (page 7, lines 122-127) as follows. “BP was measured twice by trained staff using a UA-779 digital monitor after they remained at rest in the seated position for at least 5 minutes (Longo D et al, Blood Press Monit 2002). If the difference between the two measurements was >10 mm Hg for SBP, a third measurement was required. Only the last two readings were recorded and used to calculate the average of SBP and DBP (Lacey B et al, Lancet Global Health 2018).”

We thank the reviewer for the comments. Considering the potential effect of age, sex, and survey sites for the distribution of baseline characteristics across BP categories, we conducted linear regression with adjustment for age, sex, and survey sites to analyse the distribution of continuous variables. Thus, in Table 1, we showed the mean and standard error derived from the adjusted linear model.

Following the reviewer’s suggestion, we added one table (S1 Table) in the supplementary file to describe the crude means (standard deviation) and percentages and revised the corresponding content in the manuscript (page 12, lines 229-230).

“The distribution of baseline characteristics of the study population without adjustment for age, sex, and survey sites was presented in S1 Table.”

4. line 88: "the number of hypertension" should be the prevalence of hypertension or the number of participants with hypertension.

Thank the reviewer for the correction. We have corrected the term (page 5, line 91) as follows. “…the prevalence of hypertension…”.

5. line 96: there is limited evidence for the associations of 'BP categories' with the....

Following the reviewer’s suggestion, we revised the sentence (page 5, lines 99-100) as follows. “there is limited evidence for the associations of BP categories with the major subtypes of cardiovascular disease…”

6. line 113: Check if the study was performed in accordance with the Declaration of Helsinki, and add to the end of this paragraph.

As the reviewer commented, we added one sentence in the Study population (page 6, line 116). “The study was in accordance with Declaration of Helsinki.”

The highlight of this study, as the author mentioned, the sample size was very large, and the study covered 10 regions in China.

There however remain some points that might be clarified:

1) In the methodology (lines 108-109), please add that this study was a general population study when you briefly described the CKB study. Otherwise, it will be liable to cause misunderstandings.

Thank the reviewer for pointing this out. We have revised this part (page 6, line 111) to avoid misunderstanding.

“The CKB is a community-based prospective cohort study, involving over 0.5 million adults aged 30-79 years in 10 areas of China between 2004 and 2008.”

If the difference between the two measurements was >10 mm Hg for SBP, a third measurement was required. Only the last two readings were recorded so it was not possible to calculate the average BP using three consecutive measurements. To avoid confusion, we have rephrased the assessment of BP as follows (page 7, lines 132-133).

“Only the last two readings were recorded and used to calculate the average of SBP and DBP (Lacey B et al, Lancet Global Health 2018).”

Thank the review’s comments. Considering that the burden of high BP on the health system is mainly through CVDs and the heavy burden of CVD, especially cerebrovascular diseases, in the Chinese population, we mainly focused on exploring the effect of BP categories on CVD and CVD subtypes in the current study. Moreover, we want to provide more evidence about the effect of newly defined hypertension in the 2017 ACC/AHA guideline given that previous studies showed mixed results about its effect on CVD mortality and little evidence is available for its effect on CVD subtypes. Thus, in the current study, we focused on CVD but not all-cause death. Future studies would be conducted to explore the associations of BP with all-cause and non-CVD mortality.

4) The statistical description of the constituent ratio in Figure 1 is not visualized enough using histograms, and it is recommended to modify it to a pie chart.

Thank the reviewer’s suggestion. Besides showing the proportions of different BP categories, we also want to present the trend of proportions of different BP categories with aging. Therefore, we decided to use the histogram graph to visualise and compare the height of the corresponding bar across age groups.

5) When the continuous variables in Table 1 were used for statistical description, should the standard deviation rather than the standard error be considered for the degree of dispersion?

Thank the review for pointing this out. Considering the potential effect of age, sex, and survey sites for the distribution of baseline characteristics across BP categories, we conducted linear regression with adjustment for age, sex, and survey sites to analyse the distribution of continuous variables. Thus, in Table 1, we showed the mean and standard error derived from the adjusted linear model. We added one table (S1 Table) in supplementary file to describe the crude means and standard deviations (page 12, lines 229-230).

“The distribution of baseline characteristics of the study population without adjustment for age, sex, and survey sites was presented in S1 Table.”

Thank the reviewer’s comment. In the current study, the mortality of hemorrhagic stroke is higher than that of ischemic stroke, which is consistent with previous studies conducted among a Chinese population (Yang G, et al, Lancet 2013; Zhou M, et al, Lancet 2016). Zhou M et al reported that the age-standardised death rate per 100 000 people was 62.9 for ischemic stroke vs. 94.4 for hemorrhagic stroke in 2013 among a Chinese population. We added the previous evidence in the manuscript (page 6, lines 99-100).

“…and the mortality from hemorrhagic stroke was higher than that from ischemic stroke (Zhou M, et al, Lancet 2016)”

We understand the reviewer’s concern. Our results showed that prehypertension-low was associated with the increased risk of death from hemorrhagic stroke. There were no statistically significant associations between prehypertension-low and mortality from other CVD subtypes. Given that the analyses were conducted with a sufficient sample size and the point estimates of hazard risk for other CVD subtypes were around 1, we are confident about the results that prehypertension-low was not associated with increased CVD risk except for hemorrhagic stroke. However, for prehypertension-high (newly defined as hypertension in 2017 ACC/AHA guideline), it related to all subtypes of CVDs, so we concluded that it should be paid attention to in the primary prevention of CVD.

In the Table S2, we presented the associations using the Cox regression model with adjustment for covariates or step by step to explore whether these potential confounders could change the associations. A confounder could be a risk factor (e.g., age), a preventive factor (e.g., being physically active), or a surrogate or a marker of other causes (e.g., marital status and education level) for the CVD outcomes. Thus, confounders might distort the estimate of association in one direction or another. For example, a family history of corresponding diseases might increase the risk of disease, but participants with a family history might be more likely to improve their lifestyles which could further decrease the risk. Moreover, after adjustment, both the point estimates and the confidence intervals did not change substantially.

Reviewer #3: This study, involving 0.4 million people, investigated the association between hypertension subtypes and CVD outcomes. It was well written.

There are several comments that might be taken into consideration.

1. It would be better to demonstrated the “inclusion criteria” and “exclusion criteria” of the CKB study.

We thank the reviewer’s suggestions. We added the inclusion criteria and exclusion criteria in the manuscript as follows (page 6, lines 204-206).

“All men and women aged 30-79 years who were permanently resident and without major disability in each administrative unit were identified and invited to participate (Chen Z, et al. Int J Epidemiol 2005).”

We thank the reviewer’s suggestion. In the current study, we further stratified the “prehypertension” into two groups (SBP 120−129 and DBP <80 mm Hg; SBP 130−139 and/or DBP 80−89 mm Hg) using the criteria from the 2017 ACC/AHA hypertension guideline to estimate the risk of the newly defined “Hypertension stage 1”. The cut-off for the “Hypertension stage 1” (SBP 130−139 and/or DBP 80−89 mm Hg) is different from the 2018 ESC guidelines (High-normal BP: SBP 130−139 and/or DBP 85−89 mm Hg). Thus, in the manuscript, we used the terms “prehypertension-low and prehypertension-high” to name those categories instead of using the terms from 2018 ESC guidelines.

3. Please state why patients with antihypertension treatment at baseline were excluded.

Thank the reviewer’s comment. We have stated the reason in the manuscript (page 7, lines 123-125) as follows.

“Moreover, we further excluded participants with the antihypertension treatment at baseline (n=65 168), because both dose and types of antihypertensive medications may influence BP levels and lead to misclassification of BP categories.”

4. Please state the proportion of “loss to follow-up” and how it was addressed.

By December 31, 2016, 4434 (1.03%) participants were lost to follow-up. In the analyses, if participants were lost to follow-up without information about death, the survival time was calculated from baseline to the date of the latest follow-up date. We stated the rate of loss to follow-up in the manuscript as follows (page 7, line 126; page 9, lines 176-177).

“By December 31, 2016, 4434 (1.03%) participants were lost to follow-up.”

“For the current study, survival time was calculated from baseline to the date of death, loss to follow-up, or 31 December 2016, whichever occurred first.”

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PLoS One. doi: 10.1371/journal.pone.0255373.r003

Decision Letter 1

Yan Li

3 Jun 2021

PONE-D-21-07340R1

Association between blood pressure categories and cardiovascular disease mortality in China

PLOS ONE

Dear Dr. Yu,

Please revise the manuscript according to Reviewer 3's minor comments.

Please submit your revised manuscript by Jul 18 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Yan Li, MD, PhD

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: (No Response)

Reviewer #3: There are two minor comments which need to be addressed.

1. It would cause selection bias if those patients with anti-hypertensive agents were excluded. In the current study, anti-hypertensive treatment could be an unmeasured covariate which would affect the results. It should be further discussed in the part of Discussion.

2. It’s a little bit confusing that the author defined “prehypertension-high” as “hypertension stage 1” according to ACC/AHA guideline, while the definitions of IDH and ISH were not consist with ACC/AHA guideline. It should have a much clearer expression.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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PLoS One. 2021 Jul 30;16(7):e0255373. doi: 10.1371/journal.pone.0255373.r004

Author response to Decision Letter 1

14 Jun 2021

There are two minor comments which need to be addressed.

We thank the reviewer for the valuable comments. All comments and suggestions have been carefully addressed in the revised manuscript. Details of the comments are responded to point-by-point as follows.

1. It would cause selection bias if those patients with antihypertensive agents were excluded. In the current study, antihypertensive treatment could be an unmeasured covariate which would affect the results. It should be further discussed in the part of Discussion.

We thank the reviewer's comments. We agree that excluding patients with antihypertension agents might cause selection bias given that hypertension with treatment might be more aware of their health status and more accessible to health care or might have severe symptom due to high blood pressure compared to those without treatment, which could affect future CVD risk. However, taking antihypertensive medicines would change the level of blood pressure and lead to a misclassification of hypertension subtypes. Given that it is not possible to know their blood pressure level before taking antihypertensive medicines, to exclude participants with antihypertensive medicines is more reasonable for the current study. Thus, our findings could be generalized to the population without taking antihypertensive medicines.

We added the corresponding issue in the limitations section of discussion (page 21, lines 359-364).

"Second, we excluded participants who taking antihypertensive medicines, which might cause selection bias and limit the extrapolation of our findings. However, antihypertensive medication would affect the patients' blood pressure level, leading to misclassification of BP categories. Hence, it is more reasonable to restrict study participants without antihypertensive treatment."

2. It's a little bit confusing that the author defined "prehypertension-high" as "hypertension stage 1" according to ACC/AHA guideline, while the definitions of IDH and ISH were not consist with ACC/AHA guideline. It should have a much clearer expression.

We thank the reviewer's comments. In the current study, we defined the blood pressure categories mainly referred to the JNC-7 guideline, which defined prehypertension and hypertension subtypes (i.e. ISH, IDH, and SDH). The definition was adopted in previous studies (Arima H et al, Hypertension 2012; Kelly TN et al, Circulation 2008). However, the prehypertension (i.e., SBP/DBP 120 to 139/80 to 89 mm Hg) had a broad range of blood pressure, and its effects on CVD risk were heterogeneous within prehypertension (Y Huang et al, American Heart Journal 2014). In addition, the 2017 ACC/AHA guideline newly defined the high-range blood pressure within prehypertension as hypertension. Thus, we combined the prehypertension definition in JNC-7 with the new cut-off in 2017 ACC/AHA guideline, i.e., SBP 120−129 and DBP <80 mm Hg; SBP 130−139 and/or DBP 80−89 mm Hg. We revised the manuscript (page 7, lines 127-132) to clarify our categorisation.

"According to the JNC-7, BP categories were defined into five groups 1) normal (SBP <120 and DBP <80 mm Hg); 2) prehypertension (SBP 120−139 and/or DBP 80−89 mm Hg); 3) ISH (SBP ≥140 and DBP <90 mm Hg); 4) IDH (SBP <140 and DBP ≥90 mm Hg); 5) SDH (SBP ≥140 and DBP ≥90 mm Hg) (Chobanian A V., Hypertension 2003). In the 2017 ACC/AHA hypertension guideline, hypertension was defined as SBP ≥130 mmHg and/or DPB ≥90 mmHg (Whelton PK, Hypertension 2018). To estimate the effect of "Elevated" and "Hypertension stage 1", we further divided prehypertension into prehypertension-low (equal to "Elevated", SBP 120−129 and DBP <80 mm Hg) and prehypertension-high (equal to "Hypertension stage 1", SBP 130−139 and/or DBP 80−89 mm Hg) (Whelton PK, Hypertension 2018)."

Reviewer's Responses to Questions

Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: No

For the data availability, we want to state as follows.

"According to the Regulation of the People's Republic of China on the Administration of Human Genetic Resources, we are not allowed to provide Chinese human clinical and genetic data abroad without official approval so the data in the present study cannot be shared without restrictions. However, researchers who are interested in accessing and analyzing data collected in the China Kadoorie Biobank (CKB) study may contact the data use and access committee (http://www.ckbiobank.org/site/Research/Data+Access+Policy). As stated in the policy, as data custodian, the CKB study group must maintain the integrity of the database for future use and regulate data access to comply with prior conditions agreed with the Chinese government."

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PLoS One. doi: 10.1371/journal.pone.0255373.r005

Decision Letter 2

Yan Li

15 Jul 2021

Association between blood pressure categories and cardiovascular disease mortality in China

PONE-D-21-07340R2

Dear Dr. Yu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Yan Li, MD, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: (No Response)

Reviewer #3: This study reported CVD endpoints among 0.5 million adults in primary prevention in China. It was well written and should be accepted.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #1: No

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PLoS One. doi: 10.1371/journal.pone.0255373.r006

Acceptance letter

Yan Li

19 Jul 2021

PONE-D-21-07340R2

Association between blood pressure categories and cardiovascular disease mortality in China

Dear Dr. Yu:

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on behalf of

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Checklist. STROBE statement for observational studies.

(DOCX)

Click here for additional data file.^{(20.9KB, docx)}

S1 Fig. Nelson-Aalen cumulative hazard for cardiovascular diseases according to the blood pressure categories.

(DOCX)

Click here for additional data file.^{(94.3KB, docx)}

S2 Fig. Associations of ISH, IDH, and SDH with mortality from CVDs and its major subtypes in stage 1 hypertension, stage 2 hypertension and total hypertension.

(DOCX)

Click here for additional data file.^{(333.2KB, docx)}

S1 Table. Baseline characteristics of the study population by baseline BP categories.

(DOCX)

Click here for additional data file.^{(19.9KB, docx)}

S2 Table. Associations of blood pressure categories with cardiovascular diseases mortality among 430 977 participants.

(DOCX)

Click here for additional data file.^{(17.4KB, docx)}

S3 Table. Associations of blood pressure categories with mortality from cardiovascular diseases and its major subtypes.

Values are hazard ratios (95% confidence interval).

(DOCX)

Click here for additional data file.^{(17.9KB, docx)}

S4 Table. Associations of prehypertension and hypertension subtypes with mortality from cardiovascular diseases and its major subtypes by sex.

(DOCX)

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S5 Table. Associations of prehypertension and hypertension subtypes with mortality from cardiovascular diseases by survey sites.

(DOCX)

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S6 Table. Associations of blood pressure categories with deaths due to cardiovascular diseases among participants excluding the first two years of follow-up.

(DOCX)

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S7 Table. Associations of blood pressure categories with deaths of cardiovascular diseases among non-diabetes participants at baseline.

(DOCX)

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S1 Text. Baseline questionnaire in the China Kadoorie Biobank study.

(DOCX)

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Attachment

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Data Availability Statement

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PERMALINK

Association between blood pressure categories and cardiovascular disease mortality in China

Jie Guo

Jun Lv

Yu Guo

Zheng Bian

Bang Zheng

Man Wu

Ling Yang

Yiping Chen

Jian Su

Jianqiang Zhang

Jvying Yao

Junshi Chen

Zhengming Chen

Canqing Yu

Liming Li

Roles

Abstract

Background

Methods and findings

Conclusions

Introduction

Materials and methods

Study population

Assessment of BP categories

Assessment of covariates

Assessment of outcomes

Statistical analysis

Results

Baseline characteristics of participants by BP categories

Fig 1. Percentages of blood pressure categories across different age groups.

Table 1. Baseline characteristics of the study population by baseline BP categoriesa.

Association of BP categories with CVD mortality

Table 2. Associations of BP categories with mortality from CVDs and its major subtypes among 430 977 participants.

Subgroup and sensitivity analyses

Fig 2. Associations of BP categories with mortality from CVDs and its major subtypes by age groups.

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Yan Li

Roles

Transfer Alert

Author response to Decision Letter 0

Decision Letter 1

Yan Li

Roles

Author response to Decision Letter 1

Decision Letter 2

Yan Li

Roles

Acceptance letter

Yan Li

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

Table 1. Baseline characteristics of the study population by baseline BP categories^a.